Model Comparison
LongCat-Flash-Thinking vs Qwen2.5-Omni-7B
LongCat-Flash-Thinking significantly outperforms across most benchmarks.
Performance Benchmarks
Comparative analysis across standard metrics
LongCat-Flash-Thinking outperforms in 3 benchmarks (GPQA, MMLU-Pro, MMLU-Redux), while Qwen2.5-Omni-7B is better at 0 benchmarks.
LongCat-Flash-Thinking significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
LongCat-Flash-Thinking has 553.0B more parameters than Qwen2.5-Omni-7B, making it 7900.0% larger.
Context Window
Maximum input and output token capacity
Only LongCat-Flash-Thinking specifies input context (128,000 tokens). Only LongCat-Flash-Thinking specifies output context (128,000 tokens).
Input Capabilities
Supported data types and modalities
Qwen2.5-Omni-7B supports multimodal inputs, whereas LongCat-Flash-Thinking does not.
Qwen2.5-Omni-7B can handle both text and other forms of data like images, making it suitable for multimodal applications.
LongCat-Flash-Thinking
Qwen2.5-Omni-7B
License
Usage and distribution terms
LongCat-Flash-Thinking is licensed under MIT, while Qwen2.5-Omni-7B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
LongCat-Flash-Thinking was released on 2025-09-22, while Qwen2.5-Omni-7B was released on 2025-03-27.
LongCat-Flash-Thinking is 6 months newer than Qwen2.5-Omni-7B.
Sep 22, 2025
6 months ago
5mo newerMar 27, 2025
1.1 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
Qwen2.5-Omni-7B
View detailsAlibaba Cloud / Qwen Team
Detailed Comparison
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FAQ
Common questions about LongCat-Flash-Thinking vs Qwen2.5-Omni-7B